Impact Of Risk Events Research Articles

An Optimization Framework for Risk Response Actions Selection Using Hybrid ACO and FTOPSIS

This paper presents a framework for solving risk response action selection problem by considering: (1) the impact of risk events on the project objectives, (2) the interactions between risk events and (3) management criteria and preferences. For these purposes, a framework is developed by combining an optimization-based model and a Multi Criteria Decision Making (MCDM) approach. First, in the optimization-based model, Ant Colony Optimization (ACO) is used to find the best combination of response actions which have more effects on time, cost and quality. Also, in this model, to overcome the imprecision situation resulting from lack of knowledge or insufficient data, risk parameters are determined using the fuzzy set theory. Moreover, the Design Structure Matrix (DSM) is used to capture the effect of interactions between risk events. Second, theFuzzy Technique for Order Preference by Similarity to Ideal Solution (FTOPSIS) method is used to analyze the obtained solutions by ACO, based on the other management criteria. Finally, the efficiency of the proposed framework is examined by its implementation in a real building construction project. Discussions through the case study show that using the proposed framework decision makers can evaluate more aspects of response actions.

Measuring the Impact of Risk Events and Uncertainty in Activity Durations on Project Schedule

Construction projects are usually over budget and beyond their planned schedule. One possible reason for these situations is that proper risk management techniques are not applied during the planning and execution of projects. This paper introduces a method which uses the impacts of risk in project scheduling. A simple infrastructure project is considered for analysis. Uncertainty in activity durations is assigned using various statistical distributions. Further, certain risk events are added to the project using risk register. The impact of these uncertainties in activity durations and risk events are modeled and simulated in @Risk software. The software uses Monte Carlo simulations for risk analysis. The results show that the model can predict the impact of uncertainty in activity durations on the pro-ject completion time. The additional days added to the planned schedule because of occurrence of risk events can also be predicted with confidence. The results provide confirmation that risks associated with project schedules can be managed if properly accounted for

Clothes ‘R’ Us Point-of-Sale Initiative: Managing IT Programs

The case concerns a real $25 million program consisting of nine concurrent projects to deliver and implement a custom-built in-store customer relationship management (CRM) system and a new point-of-sale system in 400 stores of a national retail chain. The name of the company has been disguised for confidentiality reasons. Once deployed, the new system should give Clothes ‘R’ Us a significant strategic advantage over competitors in the marketplace; it will increase in-store manager productivity, cut costs, and ultimately drive increased sales for the retail chain. The program is in crisis, however, because the product managers have just left to join a competitor. The explicit details of the program are given, including examples of best practice program governance and the real activity network diagram for the program. Detailed Excel spreadsheets are also provided with the actual earned value data for the program. Students analyze the spreadsheets and the data given in the case to diagnose the impact of the most recent risk event and past risk events that occurred in the program. Ultimately students must answer the essential executive questions: What is wrong with the program? How should it be fixed, and what is the impact in time and money to the program? In addition, qualitative warning signs are given throughout the case—these warning signs are red flags to executives for early proactive intervention in troubled projects.The goal of the case is to teach complex program oversight. Students analyze actual earned value data for a real $25 million program consisting of nine concurrent programs and assess the impact of risk events as they occur in the program. A key takeaway of the case is that relatively simple tools (Excel spreadsheets and time tracking) combined with good project planning can be used to effectively control very complex projects. Students also learn the qualitative warning signs within programs that can serve as early indicators of problems.

Risk-Based Surveillance Planning: A Practical Value-of-Information Approach for Data Acquisition in Producing Fields

SummarySurveillance data can be critically important in managing producing oil and gas fields. To maximize value, it is necessary to identify those surveillance opportunities that are not only informative, but materially value-adding. For surveillance decisions in producing fields, applying conventional value-of-information (VoI) methods to every individual surveillance opportunity can be too time-consuming to be practical, considering the large number of relatively small data decisions to be evaluated, each possibly addressing multiple reservoir uncertainties and supporting numerous business decisions. This paper outlines risk-based surveillance planning (RBSP), a simple approach that is based on the observation that the vast majority of surveillance opportunities in producing fields are designed to manage risks. RBSP then evaluates surveillance opportunities with VoI principles, but exploits the fact that much relevant value data already exist as byproducts of risk-management processes. RBSP has been used successfully in dozens of oil and gas fields; two case studies are described herein.Many companies use a risk-management framework that documents risk events and their causes and consequences, assesses risk probability and impact, and identifies prevention measures to reduce the probability of the risk events occurring and mitigation measures to reduce the impact of risk events should they occur. If such a framework has been used, the risk assessment provides an estimate of the expected monetary value (EMV) lost because of a risk event (= probability × impact). Risk reassessment taking into account the planned prevention and mitigation measures reveals the EMV increment attributable to the risk-management measures. RBSP assumes this to be the theoretical value of “perfect” information (VoIperfect) for the package of surveillance that supports these measures.RBSP links each potential surveillance opportunity to the risk-management plan by determining how the surveillance would (a) help create or strengthen prevention measures; (b) detect the impending occurrence of a risk event, thus enabling mitigation measures to be triggered; or (c) help monitor both prevention and mitigation measures to detect any weaknesses that require improvement. The VoIperfect of a surveillance opportunity with respect to a given risk can then be estimated by attributing to it a proportion of the total VoIperfect of the surveillance package by use of a simple criticality score to estimate how dependent the risk-management measures are on each item of surveillance. A reliability score then discounts this VoIperfect on the basis of how likely the surveillance is to deliver the necessary information in reality, yielding the value of imperfect information. Where an item of surveillance affects several risk-management measures and multiple risks, the values are summed, providing a VoI for each item of surveillance.In case studies of an asphaltene-precipitation risk in a deepwater oil field and a water-influx risk in an offshore gas field, RBSP helped to create a logical value-based rationale for surveillance decisions, and the surveillance, when implemented, did facilitate effective management of the risks and added value.

Relative uncertainty in term loan projection models: what lenders could tell risk managers

This article examines the epistemology of risk assessment in the context of financial modelling for the purposes of making loan underwriting decisions. A financing request for a company in the paper and pulp industry is considered in some detail. The paper and pulp industry was chosen because (1) it is subject to some specific risks that have been identified and studied by bankers, investors and managers of paper and pulp companies and (2) certain features of the industry enable analysts to quantify the impact of specific risk events of a given dimension on a company's future financial performance. While companies in other industries may be subject to similar risk factors, the impact of risk events may be more difficult to gauge in those industries. The ability of financial analysts to model the impact of a risk event, and hence quantify a credit risk, increases the predictive accuracy of the model. I argue that bankers and regulators should recognise the uncertainty associated with unquantifiable credit risk in financial models, and they should view this uncertainty as a credit risk factor in and of itself. Evaluating the relative degree to which credit risk is quantifiable in financial models is a potentially significant yet largely unrecognised tool for credit risk management. I consider some possible applications of this assessment tool for managing risk within the banking industry.